5G Wireless Networks Research Articles

Improved 5G network slicing for enhanced QoS against attack in SDN environment using deep learning

AbstractWithin the evolving landscape of fifth‐generation (5G) wireless networks, the introduction of network‐slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network‐slicing model integrated with an attack detection system within the 5G framework. Leveraging software‐defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks‐long short‐term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real‐time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real‐world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios.

Synergy of 6G technology and IoT networks for transformative applications

SummaryThe rapid growth of Wi‐Fi networking and intelligent systems, fueled by the emergence of the Internet of Things (IoT), has enabled significant transformative advancements. The imminent arrival of sixth‐generation (6G) wireless networks offers more opportunities as fifth‐generation (5G) wireless networks improve IoT connections. This study explores the potential advantages of 6G technology and IoT networks, focusing on edge intelligence, reconfigurable smart surfaces, Terahertz communications, and blockchain integration. The study examines potential implementation challenges and highlights the potential impact of 6G on IoT device usage. This research outlines potential applications of 6G in many sectors like the internet of vehicles, healthcare, autonomous driving, unmanned aerial vehicles, satellite television, and industrial IoT. This research elaborates on specific research topics related to 6G‐IoT technologies to enhance understanding and practical applicability.

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. Quantum computing can enhance encryption algorithms to make IoT data more secure for intelligent IoT applications. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

Quad-port 28/38 GHz Antenna with Isolation Improvement for 5G Wireless Networks

A 28/38 GHz quad-port cross-shaped antenna printed on a 0.203 mm Roger RO4003 substrate is designed and fabricated for 5G networks. The antenna consists of a rectangular patch radiator with three triangle slots to enhance the antenna's impedance matching. The four elements are arranged in a cross-shaped layout to achieve the desired compactness of 29 mm × 24.2 mm × 0.203 mm. A thin parasitic strip is inserted between the second and fourth elements to enhance the port isolation. The designed antenna is simulated by utilizing the CST simulator. The antenna achieved S11 < -10 dB at 28 / 38 GHz, with isolation ≥ 20 dB. Furthermore, the E- and H-planes within the anechoic chamber are evaluated to validate the MIMO antenna radiation patterns and gain. The MIMO parameters are calculated, simulated, tested, and its outcomes are under the standard level which confirms the MIMO diversity feature. The ECC of the structure is < 0.001, the DG ≥ 9.95 dB. and the CCL was ≤ 0.2 bit/s/Hz across the dual working band.

Progressive Pattern Interleaver with Multi-Carrier Modulation Schemes and Iterative Multi-User Detection in IoT 6G Environments with Multipath Channels.

Sixth-generation (6G) wireless networks demand a more efficient implementation of non-orthogonal multiple access (NOMA) schemes for severe multipath fading environments to serve multiple users. Using non-orthogonal multiple access (NOMA) schemes in IoT 6G networks is a promising solution to allow multiple users to share the same spectral and temporal resource, increasing spectral efficiency and improving the network's capacity. In this work, we have evaluated the performance of a novel progressive pattern interleaver (PPI) employed to distinguish the users in interleaved division multiple access (IDMA) schemes, suggested by 3GPP guidelines as a NOMA scheme, with two multi-carrier modulation schemes known as single-carrier frequency-division multiple access (SC-FDMA) and orthogonal frequency-division multiplexing (OFDM), resulting in SC-FDMA-IDMA and OFDM-IDMA schemes. Both schemes are multi-carrier schemes with orthogonal sub-carriers to deal against inter-symbol interference (ISI) and orthogonal interleavers for the simultaneous access of multiple users. It has been suggested through simulation outcomes that PPI performance is adequate with SC-FDMA-IDMA and OFDM-IDMA schemes in terms of bit error rate (BER) under multipath channel conditions. Moreover, regarding bandwidth requirement and the implementation complexity of the transmitted interleaver structure, PPI is superior to the conventional random interleaver (RI).

Distributed Foundation Models for Multi-Modal Learning in 6G Wireless Networks

Distributed Foundation Models for Multi-Modal Learning in 6G Wireless Networks

Design and Development of Dual Band Millimeter Wave Substrate Integrated Waveguide Antenna Array

Abstract New communication paradigms have emerged to make better use of the available wireless spectrum due to its scarcity. Millimeter wave high-frequency spectrum could offer a viable solution to the problem of spectrum scarcity. Millimeter wave devices and antennas are becoming increasingly popular and are used in a wide variety of applications and planned Fifth Generation (5G) wireless communication networks. In this work, we develop a Substrate Integrated Waveguide (SIW) based antenna array and millimeter-wave feeding network with the aim of achieving optimal performance. A microstrip array antenna is developed for use at millimeter wave frequencies of 28 GHz and 38 GHz. Next, an SIW array antenna will be created. For high-frequency uses, SIW technology excels due to its low loss, easy integration and high quality factor. The two unequal longitudinal slots in a slotted SIW antenna cause the structure to resonate at 28 GHz and 38 GHz. The SIW structure is fabricated by making two parallel rows of metallic vias, carefully determined through sizes to ensure minimal internal losses. A microstrip line that transitions into a SIW feeds into the proposed layout. In this paper, the authors investigate the design and construction of an integrated waveguide antenna array for use at dual millimeter-wave frequencies.

Dynamic base station allocation for 6G wireless networks through narrow neural network

The 6G wireless communication system will utilize the terahertz (THz) frequency band (0.1-10 THz) to meet customer demand for increased data rates and ultra-high-speed communication in future applications. The exponential surge in data traffic, which is supported by dynamic resource allocation. To mitigate this challenge, the use of artificial intelligence-based methods, such as narrow neural network (NNN), can help to smooth the performance of the network. In this paper, an NNN-based approach for dynamic base station allocation for 6G wireless networks is proposed 14 different 6G parameters used to train the NNN model, initially achieving an accuracy of 89.5% and an F1 score of 0.72 for 200 users. Results demonstrate the efficacy of the proposed NNN approach for dynamic decision-making in 6G networks and its potential for application in other domains where similar problems exist. Moreover, the proposed narrow neural network model shows improved results with an increase in number of users and decrease in fully connected layers and regularization strength (lambda). The validation accuracy received is 98.9% and 99.6% for thousand users with single fully connected layer, none (linear) activation function and regularization strength lambda values of 0.01 and 0.001.

Revolutionizing connectivity: Unleashing the power of 5G wireless networks enhanced by artificial intelligence for a smarter future

In the context of 5G wireless networks, this paper addresses a crucial concern—data loss in Analog-to-Digital Converters (ADCs) embedded within Multiple Input Multiple Output (MIMO) channels, a prevalent challenge in modern wireless communication systems. Despite the integration of ADCs in MIMO configurations to mitigate this issue, their implementation introduces significant complexities. In response to this, the paper introduces an innovative and efficient approach aimed at enhancing channel estimation in 5G MIMO systems. The proposed methodology relies on a heuristic-based optimization technique within a channel prediction framework. The framework uses feedback information collected through a Cascade Fusion Serial Network (CFSN) to analyze the receiver-side error ratio and estimate the channel coefficients of MIMO systems at the transmitter. An attention mechanism is incorporated into the model design through the use of a Convolutional Neural Networks - Gated Recurrent Units (CNN-GRU) and a Multi-Scaled Stacked Auto encoder. Parameters of Attention CNN-GRU, Stacked Auto encoder, and CFSN are refined using the Whale Optimization method. The optimization objective is to minimize key metrics, including Root Mean Squared Error (RMSE), Bit Error Rate (BER), and Mean Squared Error (MSE) associated with the estimated channel. Experimental evaluations underscore the efficacy of the proposed MIMO model in the context of 5G networks. The results demonstrate improved convergence rates, heightened prediction performance, and reduced computational costs compared to existing methods. This research contributes valuable insights into addressing data loss challenges specific to 5G MIMO systems, providing a pioneering solution that integrates heuristic optimization, advanced neural network architectures, and nature-inspired algorithms for parameter tuning.

Quantum Intelligence in Beyond 5G Networks: Current Progress, and Open Avenues

The emergence of fifth-generation (5G) wireless technologies leads to enlarging network complexity as a result of massive data generation, exhaustive operating costs, time, energy, and the burdens of planning and management. Artificial intelligence (AI) has been demonstrated to have a vital role in improving data analytics and decision-making in massive 5G networks. On the other hand, Quantum Computing is an evolving technology for handling exponential expansion in the data dimensions and calculating linear algebra quicker and more proficiently than traditional computers implying reduced computational costs and energy consumption. The unification between these disciplines engenders the concept of “Quantum Intelligence", which is an innovative and quite promising field with the possibility of unbounded capabilities for the 5G network. Beyond centralized learning, our discussions extend to debate the potentials of quantum intelligence to improve the distributed (federated) learning scenarios over several quantum computers, aiming to drastically enhance computational efficiency and energy consumption. Multiple simulation experiments are performed to evaluate and compare the performance of quantum intelligence on classical and quantum datasets. Finally, this article outlines the major technical and research challenges and open problems for future research on quantum intelligence in 5G wireless networks.

Improving power efficiency in 6G wireless communication networks through reconfigurable intelligent surfaces for different phase information

With increasing needs for high-bitrate, ultra-reliability, spectral efficiency, power efficiency, and reducing latency in the wireless network, global studies on the sixth generation of this network began in 2020. In this paper, we will look at intelligent reconfigurable surface structure and its application in new promising physical layer technologies, such as terahertz communications and UM-MIMO systems, to support very high-bitrate and superior network capacity in the 6G wireless communications. However, terahertz communications and UM-MIMO systems are the primary research points and confront many challenges for practical implementation. They require many RF chains and create problems in terms of cost and hardware complexity which RIS can simplify hardware and reduce cost. Therefore, we will present different modeling of wireless communication systems based on RIS for different phase information. Simulation results obtained by examining SNR performance and the error probability that shows the improvement of the received signal quality. According to results, RIS-based wireless communications can become an optimized model for future wireless communication systems.

IRS-aided NOMA-based communication architecture for 6G wireless networks: An enhanced error-control and reliable data transmission

Intelligent reflecting surface (IRS) is a key technology for sixth-generation (6G) wireless communication to obtain high energy and spectral efficiency with a minimum cost. On the other hand, non orthogonal multiple access (NOMA) enhances the spectrum efficiency of the network. The combining schemes are implemented to obtain error-free data at the receiver. This paper proposes an IRS-aided NOMA-based packet combining (PC) and aggressive packet combining (APC) schemes-assisted communication architecture to enhance service quality and achieve sustainable and resilient connectivity among users for 6G wireless networks. In this paper, the mathematical model has been derived for the instantaneous achievable data rate, bit error rate, outage probability, and packet correction capability. The derived analytical expressions are validated through detailed Monte Carlo simulations. The obtained results show that the proposed schemes outperform the conventional NOMA with PC and NOMA with APC schemes and facilitate enhanced error control and reliable data transmission capabilities. Moreover, the proposed schemes have been compared with the IRS-aided orthogonal multiple access (OMA) schemes, where IRS-aided OMA schemes are considered benchmarks of the proposed schemes. It is presented that the proposed schemes are superior to the conventional OMA with PC and APC schemes and IRS-aided OMA with PC and APC schemes.

Real-time fusion multi-tier DNN-based collaborative IDPS with complementary features for secure UAV-enabled 6G networks

UAV-enabled Integrated Sensing and Communication (ISAC) in sixth-generation (6G) wireless networks has sparked significant research interest. UAVs are positioned as aerial wireless platforms, extending coverage and improving Sensing and Communication (S&C) services. However, integrating UAVs introduces vulnerabilities due to multi-connectivity, necessitating robust security measures. To address this, efforts are focused on developing effective Intrusion Detection Systems (IDS). Here ML-based IDSs depend on the most suited Machine Learning (ML) algorithms for improved detection accuracy. However, inadequate detection features frequently contribute to the limitations of detection accuracy in various emerging cyber-attacks. Our study explores attack traits in UAV-enabled 6G networks, using complementary detection features to enhance ML-based attack detection. Additionally, existing Deep Neural Network (DNN) solutions for UAVs-enabled 6G networks mainly use single models (e.g., CNN, SVM, CGAN) instead of multi-tier models, (e.g., Hybrid, Fusion, Ensemble). Here, although the use of a single model can be faster for computer systems, it is difficult to understand the growing complexity of intrusion patterns in data. Also, these single models might not be good at recognizing the unique patterns of less common issues in datasets. Thus, we propose a fusion multi-tier DNN-based Collaborative Intrusion Detection and Prevention System (CIDPS) for critical UAV-enabled 6G networks. This system boosts accuracy without sacrificing latency reductions. Our approach learns decision boundaries from imbalanced data points using preceding DNNs sequentially. Moreover, most existing research works do not focus on intrusion prevention methods and deployment frameworks for UAV networks. This research proposes effective IPS mechanisms and deployment architecture for CIDPS in UAV-enabled 6G networks. It incorporates a CIDPS with an emergency response protocol, neutralizing attacks upon anomaly detection. We validate our solution through diverse dataset experiments (UAVIDS-2020, NF-UQ-NIDS-v2, 5G-NIDD). Unlike traditional practices where IDS are simulated, we implement CIDPS on actual UAV devices (PX4 Vision Dev Kit V1.5, DJI mini se, DJI mini 3 pro) and real-world UAV networks. Our approach outperforms prior algorithms with a 99.25% attack classification accuracy, higher detection efficiency, and lower resource usage, exceeding 99.05% detection rates.

Demonstration of photonics-based D-band integrated localization and communication

The terahertz spectrum has the ability to provide high-speed communication and millimeter-level resolution. As a result, terahertz-integrated sensing and communication (ISAC) has been identified as a key enabler for 6G wireless networks. This work discusses a photonics-based D-band communication system for integrated high-resolution localization and high-speed wireless communication. Our empirical results show that a communication rate of 5 Gbps over a distance of 1.5 m and location identification of the target with millimeter-level (&lt;4mm) range resolution can be conducted simultaneously using the same signal. We also show that the error due to the thickness of the beam splitter can be eliminated, while the quantization error and the random drift errors are the limiting factors of the resolution achieved. This experimental demonstration using D-band communication indicates that terahertz ISAC can be realized for 6G networks while considering the underlying system restrictions (e.g., bandwidth limit and lens diameter).

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

Emerging near‐infrared luminescent materials for next‐generation broadband optical communications

AbstractThe rapid development of emerging technologies observed in recent years, such as artificial intelligence, machine learning, mobile internet, big data, cloud computing, and the Internet of Everything, are generating escalating demands for expanding the capacity density, and speed in next‐generation optical communications. This poses a significant challenge to existing communication techniques. Within this context, the integration of near‐infrared broadband, tunable, and high‐gain luminescent materials into silicon optical circuits or fiber architectures to transmit and modulate light shows enormous potential for advancing next‐generation communication techniques. Here, this review provides an overview of the recent breakthroughs in near‐infrared luminescent epitaxial/colloidal quantum dots, and metal‐active‐center‐doped materials for broadband optical amplifiers and tunable lasers. We also expound on efforts to enhance the bandwidth and gain of these materials‐based amplifiers and lasers, exploring the challenges associate with developing ultra‐broadband and high‐speed optical communication systems. Additionally, the potential applications in Fifth Generation Fixed Networks, integration with 5G and 6G wireless networks, compensation for current Si electronic based CMOS for high computing capability, and the prospects of these light sources for next‐generation optoelectronic devices are discussed.image

Simulation and Analysis of the Code Domain NOMA with UFMC for 5G Wireless Networks

In fifth generation (5G) wireless networks, radio access techniques and multi-carrier waveforms play a vital role in meeting the diversified demands of ultra-low latency, massive connectivity, and higher throughput. Multi-access schemes used conventionally in 4G system was Orthogonal Multiple Access (OMA) technique. The OMA techniques suffer from inefficient spectrum utilization, high latency, and supports a limited number of users. Next-generation networks, Non-Orthogonal Multiple Access (NOMA), has a great potential, in which multiple users are simultaneously served using the same time, frequency, or code resource increasing the throughput. Code domain-NOMA (CD-NOMA) is the key technique implemented in the system design where multiple users are distinguished based on unique user-specific spreading codes. The NOMA system could significantly benefit from Universal Filtered Multi-Carrier (UFMC) modulation waveform in terms of flexibility, spectral efficiency, compatibility with Multiple Input Multiple Output (MIMO) technique, and relaxed synchronization requirements. The novel integrated system proposed in the paper is CD-NOMA-UFMC with convolutional codes. The major outcome of the paper is that the combination of UFMC air interface modulation technique with CD-NOMA access method can be the most effective way to meet the growing demands of 5G.

Novel Orthogonal Time Frequency Space Based on Universal Filter Multi Carrier for 6G Wireless Networks

Obtaining an efficient multicarrier signalling technique is an essential part in signal processing of the (6G) wireless networks. In order to achieve their intended objectives, the 6G mobile networks needs the utilization of a multicarrier scheme that possesses the ability to withstand multipath fading and inter-symbol interference (ISI), as well as exhibit resilience against inter-carrier interference (ICI) and Doppler spread. In recent research, several filter-based methods have been suggested, including Filter Bank Multicarrier (FBMC), as well as various Orthogonal Frequency Division Multiplexing (OFDM) based techniques such as Universal Filtered Multicarrier (UFMC). Orthogonal Time-Frequency Space (OTFS) technique has been identified as a promising candidate waveform for 6G, particularly in high-mobility scenarios, where it has demonstrated superior performance compared to traditional OFDM-based techniques. The OTFS method offers an advantage in that it can be considered the pre-and post-processing stages for techniques based on OFDM. This papaer presents the design (OTFS) using (UFMC) technique. Additionally, a novel structure is proposed for coded/uncoded UFMC-based OTFS technique, which combines the advantages of both strategies while mitigating their limitations. The results of our analysis demonstrate that the UFMC-based OTFS system exhibits superior performance in terms of error probability and time-spectral efficiency compared to the OFDM-based OTFS system.

Pilot and data power optimization problems in multiple Aerial Relay Stations cell‐free communication systems

SummaryA cell‐free (CF) technology and unmanned aerial vehicles (UAVs) acting as aerial relay stations (ARSs) are gradually viewed as viable technologies for usage in sixth‐generation (6G) wireless networks owing to their outstanding advantages, such as large coverage, uniform service quality, huge connections, flexible deployment in all geographical areas, and high line‐of‐sight (LoS) probability. As a result, the combination of the CF model and UAVs is suitable for densely populated urban areas, shopping malls, festivals, and locations with complex geography. Furthermore, the integration of the CF model and UAV communication can improve system quality and is considered an entirely new research direction as there is no comprehensive investigation of this combined model. In this study, we investigate the quality of the multi‐ARS CF communication system, where ARSs are equipped with multiple antennas and stochastic distribution within a specific region to simultaneously serve numerous ground users. We establish a closed‐form formulation for the uplink and downlink throughput of individual users by using the matched filtering method and conjugate beamforming technology, respectively. Moreover, we propose a novel method to optimize the pilot and data transmission power coefficients for improving channel estimation and increasing throughput per user, thereby ensuring a high quality of service. The power optimization method is executed via the successive convex approximation (SCA) method, second‐order cone program (SOCP), and linear program. We evaluate system performance according to the cumulative distribution function (CDF) of user throughput based on various parameters, such as the number of users, the number of ARSs, and the length of pilot sequences. The analytical findings also reveal that the system with the proposed power optimization method is always superior to the system without the proposed method in both uplink and downlink.

Multi-Dimensional Multiple Access (MDMA) Scheme Provisioning in 6G Wireless Networks

Multi-Dimensional Multiple Access (MDMA) Scheme Provisioning in 6G Wireless Networks